Overview

Dataset statistics

Number of variables20
Number of observations295747
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.1 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

Time (s) is highly overall correlated with CO (ppm) and 2 other fieldsHigh correlation
CO (ppm) is highly overall correlated with Time (s) and 9 other fieldsHigh correlation
Temperature (C) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Heater voltage (V) is highly overall correlated with R4 (MOhm) and 9 other fieldsHigh correlation
R1 (MOhm) is highly overall correlated with R2 (MOhm) and 8 other fieldsHigh correlation
R2 (MOhm) is highly overall correlated with R1 (MOhm) and 5 other fieldsHigh correlation
R3 (MOhm) is highly overall correlated with R1 (MOhm) and 9 other fieldsHigh correlation
R4 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R5 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R6 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R7 (MOhm) is highly overall correlated with R1 (MOhm) and 12 other fieldsHigh correlation
R8 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R9 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R10 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R11 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R12 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R13 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R14 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
Humidity (%r.h.) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Flow rate (mL/min) is highly skewed (γ1 = -102.280061)Skewed
Time (s) is uniformly distributedUniform
Time (s) has unique valuesUnique
CO (ppm) has 32165 (10.9%) zerosZeros

Reproduction

Analysis started2022-12-20 08:39:46.116746
Analysis finished2022-12-20 08:41:36.957364
Duration1 minute and 50.84 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Time (s)
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct295747
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45457.968
Minimum0
Maximum90909.582
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:37.094790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4558.0795
Q122747.061
median45445.015
Q368189.563
95-th percentile86360.519
Maximum90909.582
Range90909.582
Interquartile range (IQR)45442.501

Descriptive statistics

Standard deviation26237.076
Coefficient of variation (CV)0.57717221
Kurtosis-1.1997829
Mean45457.968
Median Absolute Deviation (MAD)22721.288
Skewness0.00024854152
Sum1.3444058 × 1010
Variance6.8838415 × 108
MonotonicityStrictly increasing
2022-12-20T14:11:37.343887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
60634.305 1
 
< 0.1%
60605.885 1
 
< 0.1%
60605.575 1
 
< 0.1%
60605.267 1
 
< 0.1%
60604.958 1
 
< 0.1%
60604.649 1
 
< 0.1%
60604.339 1
 
< 0.1%
60604.03 1
 
< 0.1%
60603.722 1
 
< 0.1%
Other values (295737) 295737
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.308 1
< 0.1%
0.617 1
< 0.1%
0.925 1
< 0.1%
1.235 1
< 0.1%
1.542 1
< 0.1%
1.852 1
< 0.1%
2.161 1
< 0.1%
2.471 1
< 0.1%
2.781 1
< 0.1%
ValueCountFrequency (%)
90909.582 1
< 0.1%
90909.273 1
< 0.1%
90908.965 1
< 0.1%
90908.655 1
< 0.1%
90908.346 1
< 0.1%
90908.039 1
< 0.1%
90907.73 1
< 0.1%
90907.422 1
< 0.1%
90907.112 1
< 0.1%
90906.805 1
< 0.1%

CO (ppm)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct301
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9010757
Minimum0
Maximum20
Zeros32165
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:37.504300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.44
median8.89
Q315.56
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11.12

Descriptive statistics

Standard deviation6.4266404
Coefficient of variation (CV)0.64908507
Kurtosis-1.232982
Mean9.9010757
Median Absolute Deviation (MAD)6.67
Skewness0.0088063379
Sum2928213.4
Variance41.301707
MonotonicityNot monotonic
2022-12-20T14:11:37.662717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32165
10.9%
13.33 29311
9.9%
4.44 29270
9.9%
11.11 29269
9.9%
17.78 29262
9.9%
8.89 29248
9.9%
2.22 29238
9.9%
20 29234
9.9%
6.67 29231
9.9%
15.56 29225
9.9%
Other values (291) 294
 
0.1%
ValueCountFrequency (%)
0 32165
10.9%
0.0778 1
 
< 0.1%
0.0932 1
 
< 0.1%
0.12 1
 
< 0.1%
0.3645 1
 
< 0.1%
0.373 1
 
< 0.1%
0.4534 1
 
< 0.1%
0.4669 1
 
< 0.1%
0.4973 1
 
< 0.1%
0.4999 1
 
< 0.1%
ValueCountFrequency (%)
20 29234
9.9%
19.6492 1
 
< 0.1%
19.5799 1
 
< 0.1%
19.4406 1
 
< 0.1%
19.3873 1
 
< 0.1%
19.2 1
 
< 0.1%
19.1335 1
 
< 0.1%
18.8354 1
 
< 0.1%
18.7546 1
 
< 0.1%
18.68 1
 
< 0.1%

Humidity (%r.h.)
Real number (ℝ)

Distinct22121
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.840798
Minimum16.37
Maximum71.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:37.833277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.37
5-th percentile22.72778
Q135.52
median46.02
Q354.26
95-th percentile64.09
Maximum71.84
Range55.47
Interquartile range (IQR)18.74

Descriptive statistics

Standard deviation12.309527
Coefficient of variation (CV)0.27451624
Kurtosis-0.73732151
Mean44.840798
Median Absolute Deviation (MAD)9.24
Skewness-0.15049323
Sum13261532
Variance151.52446
MonotonicityNot monotonic
2022-12-20T14:11:37.980486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.91 4281
 
1.4%
37.11 2862
 
1.0%
30.68 2266
 
0.8%
29.65 2253
 
0.8%
36.04 1982
 
0.7%
32.83 1830
 
0.6%
37.15 1731
 
0.6%
51.24 1730
 
0.6%
47.62 1656
 
0.6%
47.64 1633
 
0.6%
Other values (22111) 273523
92.5%
ValueCountFrequency (%)
16.37 884
0.3%
16.3702 1
 
< 0.1%
16.3705 2
 
< 0.1%
16.3709 1
 
< 0.1%
16.3711 1
 
< 0.1%
16.3716 1
 
< 0.1%
16.3829 1
 
< 0.1%
16.4298 1
 
< 0.1%
16.5062 1
 
< 0.1%
16.5555 1
 
< 0.1%
ValueCountFrequency (%)
71.84 144
< 0.1%
71.8399 2
 
< 0.1%
71.8398 1
 
< 0.1%
71.8396 1
 
< 0.1%
71.8395 1
 
< 0.1%
71.8394 1
 
< 0.1%
71.8393 1
 
< 0.1%
71.8392 1
 
< 0.1%
71.839 1
 
< 0.1%
71.8362 1
 
< 0.1%

Temperature (C)
Real number (ℝ)

Distinct6372
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.816283
Minimum25.14
Maximum26.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:38.138152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum25.14
5-th percentile25.46
Q125.58
median25.82
Q326.1
95-th percentile26.22
Maximum26.3
Range1.16
Interquartile range (IQR)0.52

Descriptive statistics

Standard deviation0.28044331
Coefficient of variation (CV)0.01086304
Kurtosis-1.189056
Mean25.816283
Median Absolute Deviation (MAD)0.24
Skewness-0.0014188631
Sum7635088.2
Variance0.07864845
MonotonicityNot monotonic
2022-12-20T14:11:38.401021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.5 29462
 
10.0%
25.58 21662
 
7.3%
26.18 19560
 
6.6%
26.22 19498
 
6.6%
26.06 17943
 
6.1%
25.82 17068
 
5.8%
26.14 15877
 
5.4%
25.62 15814
 
5.3%
25.66 14688
 
5.0%
25.86 13104
 
4.4%
Other values (6362) 111071
37.6%
ValueCountFrequency (%)
25.14 889
0.3%
25.1401 50
 
< 0.1%
25.1402 6
 
< 0.1%
25.1409 1
 
< 0.1%
25.141 1
 
< 0.1%
25.1411 1
 
< 0.1%
25.1414 1
 
< 0.1%
25.1418 3
 
< 0.1%
25.1423 2
 
< 0.1%
25.1427 1
 
< 0.1%
ValueCountFrequency (%)
26.3 226
0.1%
26.2999 2
 
< 0.1%
26.2987 1
 
< 0.1%
26.2972 2
 
< 0.1%
26.297 2
 
< 0.1%
26.2958 1
 
< 0.1%
26.2956 1
 
< 0.1%
26.2951 1
 
< 0.1%
26.2948 2
 
< 0.1%
26.2945 3
 
< 0.1%

Flow rate (mL/min)
Real number (ℝ)

Distinct11422
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.9441
Minimum0
Maximum271.5177
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:38.560453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile239.7537
Q1239.8959
median239.9716
Q3240.0448
95-th percentile240.1809
Maximum271.5177
Range271.5177
Interquartile range (IQR)0.1489

Descriptive statistics

Standard deviation1.8815109
Coefficient of variation (CV)0.0078414549
Kurtosis12080.021
Mean239.9441
Median Absolute Deviation (MAD)0.0744
Skewness-102.28006
Sum70962748
Variance3.5400831
MonotonicityNot monotonic
2022-12-20T14:11:38.723489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239.9601 145
 
< 0.1%
239.993 145
 
< 0.1%
239.9725 139
 
< 0.1%
239.9891 137
 
< 0.1%
239.964 136
 
< 0.1%
239.971 135
 
< 0.1%
239.9713 134
 
< 0.1%
239.9664 134
 
< 0.1%
239.985 133
 
< 0.1%
239.9625 133
 
< 0.1%
Other values (11412) 294376
99.5%
ValueCountFrequency (%)
0 8
< 0.1%
0.1077 1
 
< 0.1%
0.4031 1
 
< 0.1%
0.7013 1
 
< 0.1%
10.0643 1
 
< 0.1%
24.0524 1
 
< 0.1%
54.1133 1
 
< 0.1%
84.3198 1
 
< 0.1%
96.2955 1
 
< 0.1%
105.9781 1
 
< 0.1%
ValueCountFrequency (%)
271.5177 1
< 0.1%
264.9491 1
< 0.1%
263.5318 1
< 0.1%
261.1356 1
< 0.1%
257.6915 1
< 0.1%
257.1371 1
< 0.1%
256.8458 1
< 0.1%
256.5268 1
< 0.1%
256.1471 1
< 0.1%
255.9373 1
< 0.1%

Heater voltage (V)
Real number (ℝ)

Distinct1731
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35492141
Minimum0.198
Maximum0.8999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:38.881184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.198
5-th percentile0.1991
Q10.2
median0.2
Q30.207
95-th percentile0.8983
Maximum0.8999
Range0.7019
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.28839291
Coefficient of variation (CV)0.81255429
Kurtosis-0.20646774
Mean0.35492141
Median Absolute Deviation (MAD)0.0002
Skewness1.3368572
Sum104966.94
Variance0.083170473
MonotonicityNot monotonic
2022-12-20T14:11:39.031979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 136148
46.0%
0.898 27959
 
9.5%
0.199 9971
 
3.4%
0.1995 5560
 
1.9%
0.1997 5547
 
1.9%
0.1994 5535
 
1.9%
0.1999 5513
 
1.9%
0.1992 5495
 
1.9%
0.1996 5493
 
1.9%
0.1993 5483
 
1.9%
Other values (1721) 83043
28.1%
ValueCountFrequency (%)
0.198 2
< 0.1%
0.1981 3
< 0.1%
0.1982 3
< 0.1%
0.1983 3
< 0.1%
0.1984 1
 
< 0.1%
0.1985 2
< 0.1%
0.1986 3
< 0.1%
0.1987 1
 
< 0.1%
0.1988 2
< 0.1%
0.1989 3
< 0.1%
ValueCountFrequency (%)
0.8999 9
 
< 0.1%
0.8998 4
 
< 0.1%
0.8997 4
 
< 0.1%
0.8996 3
 
< 0.1%
0.8995 3
 
< 0.1%
0.8994 7
 
< 0.1%
0.8993 4
 
< 0.1%
0.8992 5
 
< 0.1%
0.8991 5
 
< 0.1%
0.899 4890
1.7%

R1 (MOhm)
Real number (ℝ)

Distinct8515
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.184218
Minimum0.0327
Maximum117.8584
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:39.193479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0327
5-th percentile0.079
Q10.4381
median2.294
Q331.0916
95-th percentile71.3877
Maximum117.8584
Range117.8257
Interquartile range (IQR)30.6535

Descriptive statistics

Standard deviation24.444432
Coefficient of variation (CV)1.4224932
Kurtosis0.70362257
Mean17.184218
Median Absolute Deviation (MAD)2.2123
Skewness1.3682745
Sum5082180.8
Variance597.53027
MonotonicityNot monotonic
2022-12-20T14:11:39.351080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.5638 787
 
0.3%
75.0345 765
 
0.3%
73.7788 761
 
0.3%
71.9176 740
 
0.3%
76.3332 737
 
0.2%
74.3444 732
 
0.2%
75.6194 713
 
0.2%
73.1111 712
 
0.2%
70.2486 675
 
0.2%
71.3877 675
 
0.2%
Other values (8505) 288450
97.5%
ValueCountFrequency (%)
0.0327 1
< 0.1%
0.0334 1
< 0.1%
0.0335 2
< 0.1%
0.0336 1
< 0.1%
0.0344 1
< 0.1%
0.0345 1
< 0.1%
0.0347 2
< 0.1%
0.0348 1
< 0.1%
0.0349 1
< 0.1%
0.0351 1
< 0.1%
ValueCountFrequency (%)
117.8584 2
 
< 0.1%
116.4568 2
 
< 0.1%
114.818 8
 
< 0.1%
113.4868 8
 
< 0.1%
111.9292 11
 
< 0.1%
110.6632 27
< 0.1%
109.181 26
< 0.1%
107.9756 37
< 0.1%
106.5634 46
< 0.1%
105.4143 52
< 0.1%

R2 (MOhm)
Real number (ℝ)

Distinct8213
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.152156
Minimum0.0571
Maximum144.618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:39.513266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0571
5-th percentile0.1407
Q10.5084
median1.684
Q335.1959
95-th percentile79.7171
Maximum144.618
Range144.5609
Interquartile range (IQR)34.6875

Descriptive statistics

Standard deviation28.050854
Coefficient of variation (CV)1.4646316
Kurtosis0.19446983
Mean19.152156
Median Absolute Deviation (MAD)1.5418
Skewness1.2843257
Sum5664192.8
Variance786.85043
MonotonicityNot monotonic
2022-12-20T14:11:39.668069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.1822 1178
 
0.4%
82.7015 1168
 
0.4%
82.004 1152
 
0.4%
80.5097 1115
 
0.4%
79.7171 1090
 
0.4%
83.5541 1078
 
0.4%
78.3034 1058
 
0.4%
77.677 1040
 
0.4%
79.0683 1023
 
0.3%
76.3332 972
 
0.3%
Other values (8203) 284873
96.3%
ValueCountFrequency (%)
0.0571 1
 
< 0.1%
0.0575 1
 
< 0.1%
0.0582 1
 
< 0.1%
0.0594 1
 
< 0.1%
0.0595 1
 
< 0.1%
0.0602 2
< 0.1%
0.0606 1
 
< 0.1%
0.0607 2
< 0.1%
0.0608 1
 
< 0.1%
0.0612 3
< 0.1%
ValueCountFrequency (%)
144.618 1
 
< 0.1%
121.0628 2
 
< 0.1%
119.5851 2
 
< 0.1%
117.8584 2
 
< 0.1%
116.4568 2
 
< 0.1%
114.818 4
 
< 0.1%
113.4868 7
 
< 0.1%
111.9292 16
< 0.1%
110.6632 28
< 0.1%
109.181 25
< 0.1%

R3 (MOhm)
Real number (ℝ)

Distinct8226
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.683613
Minimum0.0547
Maximum222.9919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:39.840302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0547
5-th percentile0.1121
Q10.6236
median5.2703
Q348.4112
95-th percentile83.0507
Maximum222.9919
Range222.9372
Interquartile range (IQR)47.7876

Descriptive statistics

Standard deviation29.468287
Coefficient of variation (CV)1.244248
Kurtosis-0.48673673
Mean23.683613
Median Absolute Deviation (MAD)5.1562
Skewness0.96914527
Sum7004357.4
Variance868.37995
MonotonicityNot monotonic
2022-12-20T14:11:39.990540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.7703 1188
 
0.4%
85.3974 1183
 
0.4%
82.2033 1170
 
0.4%
84.6501 1151
 
0.4%
86.3116 1134
 
0.4%
83.0507 1118
 
0.4%
80.6932 1104
 
0.4%
81.51 1080
 
0.4%
87.0883 1050
 
0.4%
88.0388 1035
 
0.3%
Other values (8216) 284534
96.2%
ValueCountFrequency (%)
0.0547 1
< 0.1%
0.0564 1
< 0.1%
0.0568 1
< 0.1%
0.0576 1
< 0.1%
0.0578 1
< 0.1%
0.0579 2
< 0.1%
0.0584 1
< 0.1%
0.0585 1
< 0.1%
0.0586 1
< 0.1%
0.0589 1
< 0.1%
ValueCountFrequency (%)
222.9919 1
 
< 0.1%
198.6068 1
 
< 0.1%
190.1297 1
 
< 0.1%
156.6533 2
 
< 0.1%
146.3314 1
 
< 0.1%
131.01 1
 
< 0.1%
129.2732 1
 
< 0.1%
123.6951 1
 
< 0.1%
114.1263 2
 
< 0.1%
112.8031 9
< 0.1%

R4 (MOhm)
Real number (ℝ)

Distinct7590
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.898428
Minimum0.0398
Maximum90.6217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:40.150754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0398
5-th percentile0.102
Q12.2243
median23.8181
Q336.8645
95-th percentile54.0817
Maximum90.6217
Range90.5819
Interquartile range (IQR)34.6402

Descriptive statistics

Standard deviation18.614631
Coefficient of variation (CV)0.81292179
Kurtosis-0.90366848
Mean22.898428
Median Absolute Deviation (MAD)16.3449
Skewness0.2951025
Sum6772141.4
Variance346.50449
MonotonicityNot monotonic
2022-12-20T14:11:40.402717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.257 1117
 
0.4%
36.8645 1110
 
0.4%
36.3021 1108
 
0.4%
36.4987 1085
 
0.4%
37.0331 1076
 
0.4%
36.1399 1070
 
0.4%
34.7712 1069
 
0.4%
34.2691 1066
 
0.4%
34.9216 1063
 
0.4%
35.7879 1060
 
0.4%
Other values (7580) 284923
96.3%
ValueCountFrequency (%)
0.0398 1
< 0.1%
0.04 1
< 0.1%
0.0401 2
< 0.1%
0.0409 1
< 0.1%
0.041 1
< 0.1%
0.0411 2
< 0.1%
0.0413 1
< 0.1%
0.0417 2
< 0.1%
0.0418 1
< 0.1%
0.0419 2
< 0.1%
ValueCountFrequency (%)
90.6217 1
 
< 0.1%
88.499 1
 
< 0.1%
85.5816 2
 
< 0.1%
84.5361 3
 
< 0.1%
83.6839 2
 
< 0.1%
82.6836 7
 
< 0.1%
81.8678 17
 
< 0.1%
80.9098 24
< 0.1%
80.1281 28
< 0.1%
79.2097 46
< 0.1%

R5 (MOhm)
Real number (ℝ)

Distinct7826
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.552124
Minimum0.0491
Maximum159.8474
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:40.555518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0491
5-th percentile0.1154
Q12.00325
median36.0127
Q354.3512
95-th percentile81.8393
Maximum159.8474
Range159.7983
Interquartile range (IQR)52.34795

Descriptive statistics

Standard deviation28.164234
Coefficient of variation (CV)0.83941732
Kurtosis-1.0287337
Mean33.552124
Median Absolute Deviation (MAD)25.9941
Skewness0.2909278
Sum9922940.1
Variance793.22408
MonotonicityNot monotonic
2022-12-20T14:11:40.712037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.7203 1424
 
0.5%
50.8849 1420
 
0.5%
50.562 1403
 
0.5%
49.4117 1387
 
0.5%
52.3895 1365
 
0.5%
51.1571 1357
 
0.5%
53.0277 1357
 
0.5%
54.0448 1344
 
0.5%
52.1043 1343
 
0.5%
49.9804 1339
 
0.5%
Other values (7816) 282008
95.4%
ValueCountFrequency (%)
0.0491 2
< 0.1%
0.0494 1
< 0.1%
0.0497 1
< 0.1%
0.0503 2
< 0.1%
0.0505 2
< 0.1%
0.0506 1
< 0.1%
0.0508 1
< 0.1%
0.0511 2
< 0.1%
0.0513 1
< 0.1%
0.0514 1
< 0.1%
ValueCountFrequency (%)
159.8474 1
 
< 0.1%
135.5445 1
 
< 0.1%
127.7625 1
 
< 0.1%
126.116 1
 
< 0.1%
124.1949 6
 
< 0.1%
122.6378 8
 
< 0.1%
120.8197 7
 
< 0.1%
119.345 11
< 0.1%
117.6218 20
< 0.1%
116.223 21
< 0.1%

R6 (MOhm)
Real number (ℝ)

Distinct7817
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.296268
Minimum0.0483
Maximum138.2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:40.883533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0483
5-th percentile0.1243
Q11.6929
median26.1194
Q351.7707
95-th percentile80.6531
Maximum138.2019
Range138.1536
Interquartile range (IQR)50.0778

Descriptive statistics

Standard deviation27.923778
Coefficient of variation (CV)0.92169035
Kurtosis-0.95099017
Mean30.296268
Median Absolute Deviation (MAD)24.877
Skewness0.48926737
Sum8960030.3
Variance779.73737
MonotonicityNot monotonic
2022-12-20T14:11:41.035432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.0116 1267
 
0.4%
81.5165 1220
 
0.4%
49.8802 1217
 
0.4%
51.7707 1216
 
0.4%
53.1112 1206
 
0.4%
47.8646 1206
 
0.4%
50.2138 1202
 
0.4%
50.4951 1194
 
0.4%
48.6935 1193
 
0.4%
50.8369 1179
 
0.4%
Other values (7807) 283647
95.9%
ValueCountFrequency (%)
0.0483 1
 
< 0.1%
0.0485 2
< 0.1%
0.0487 1
 
< 0.1%
0.0489 1
 
< 0.1%
0.0496 2
< 0.1%
0.0497 1
 
< 0.1%
0.05 1
 
< 0.1%
0.0503 1
 
< 0.1%
0.0505 4
< 0.1%
0.0507 1
 
< 0.1%
ValueCountFrequency (%)
138.2019 1
 
< 0.1%
122.0913 1
 
< 0.1%
120.179 2
 
< 0.1%
118.6302 3
 
< 0.1%
116.8232 4
 
< 0.1%
115.3585 7
 
< 0.1%
113.6483 7
 
< 0.1%
112.2611 25
 
< 0.1%
110.6402 55
< 0.1%
109.3244 74
< 0.1%

R7 (MOhm)
Real number (ℝ)

Distinct7704
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.714225
Minimum0.054
Maximum193.5002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:41.207623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.054
5-th percentile0.1226
Q12.1511
median35.3355
Q355.0846
95-th percentile82.883
Maximum193.5002
Range193.4462
Interquartile range (IQR)52.9335

Descriptive statistics

Standard deviation28.421584
Coefficient of variation (CV)0.8430146
Kurtosis-1.0836415
Mean33.714225
Median Absolute Deviation (MAD)26.4465
Skewness0.29014119
Sum9970880.9
Variance807.78643
MonotonicityNot monotonic
2022-12-20T14:11:41.369080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.7898 1491
 
0.5%
53.0601 1435
 
0.5%
51.1767 1425
 
0.5%
51.4536 1412
 
0.5%
53.7185 1395
 
0.5%
52.0732 1389
 
0.5%
50.8483 1388
 
0.5%
53.3574 1376
 
0.5%
49.9924 1357
 
0.5%
54.7053 1356
 
0.5%
Other values (7694) 281723
95.3%
ValueCountFrequency (%)
0.054 1
 
< 0.1%
0.0543 1
 
< 0.1%
0.0544 1
 
< 0.1%
0.0547 2
< 0.1%
0.0552 1
 
< 0.1%
0.0556 1
 
< 0.1%
0.0558 1
 
< 0.1%
0.0562 1
 
< 0.1%
0.0563 2
< 0.1%
0.0564 4
< 0.1%
ValueCountFrequency (%)
193.5002 1
 
< 0.1%
137.0008 1
 
< 0.1%
125.3547 1
 
< 0.1%
118.6246 1
 
< 0.1%
116.9118 1
 
< 0.1%
115.5214 4
 
< 0.1%
113.8957 10
 
< 0.1%
112.5752 26
< 0.1%
111.0301 30
< 0.1%
109.7743 62
< 0.1%

R8 (MOhm)
Real number (ℝ)

Distinct6196
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.70001
Minimum0.0331
Maximum112.891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:41.543180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0331
5-th percentile0.0998
Q112.47
median28.3556
Q343.5025
95-th percentile65.0072
Maximum112.891
Range112.8579
Interquartile range (IQR)31.0325

Descriptive statistics

Standard deviation21.084326
Coefficient of variation (CV)0.73464524
Kurtosis-0.78983468
Mean28.70001
Median Absolute Deviation (MAD)15.4071
Skewness0.18716603
Sum8487941.9
Variance444.54879
MonotonicityNot monotonic
2022-12-20T14:11:41.695293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.5445 1075
 
0.4%
51.311 1074
 
0.4%
54.073 1070
 
0.4%
47.9632 1060
 
0.4%
55.988 1051
 
0.4%
49.415 1048
 
0.4%
56.4158 1048
 
0.4%
53.3555 1044
 
0.4%
53.0355 1040
 
0.4%
51.6712 1035
 
0.3%
Other values (6186) 285202
96.4%
ValueCountFrequency (%)
0.0331 1
 
< 0.1%
0.0336 1
 
< 0.1%
0.0337 1
 
< 0.1%
0.0339 1
 
< 0.1%
0.0343 2
< 0.1%
0.0344 1
 
< 0.1%
0.0345 1
 
< 0.1%
0.0346 1
 
< 0.1%
0.0347 3
< 0.1%
0.0348 1
 
< 0.1%
ValueCountFrequency (%)
112.891 1
 
< 0.1%
100.3018 2
 
< 0.1%
97.8971 2
 
< 0.1%
95.604 3
 
< 0.1%
94.5965 7
 
< 0.1%
93.4149 4
 
< 0.1%
92.4523 13
< 0.1%
91.3229 11
< 0.1%
90.4024 12
< 0.1%
89.3217 22
< 0.1%

R9 (MOhm)
Real number (ℝ)

Distinct6175
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.435529
Minimum0.0293
Maximum102.6635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:41.855932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0293
5-th percentile0.0967
Q18.4984
median22.6925
Q337.6116
95-th percentile59.6029
Maximum102.6635
Range102.6342
Interquartile range (IQR)29.1132

Descriptive statistics

Standard deviation19.233828
Coefficient of variation (CV)0.7871255
Kurtosis-0.70284059
Mean24.435529
Median Absolute Deviation (MAD)14.7628
Skewness0.40196047
Sum7226734.5
Variance369.94015
MonotonicityNot monotonic
2022-12-20T14:11:42.014002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0973 2983
 
1.0%
0.0972 2943
 
1.0%
0.0975 2871
 
1.0%
0.097 2809
 
0.9%
0.0971 2802
 
0.9%
0.0976 2752
 
0.9%
0.0977 2680
 
0.9%
0.0968 2522
 
0.9%
0.0978 2429
 
0.8%
0.0967 2186
 
0.7%
Other values (6165) 268770
90.9%
ValueCountFrequency (%)
0.0293 1
 
< 0.1%
0.0294 2
 
< 0.1%
0.0296 1
 
< 0.1%
0.0297 2
 
< 0.1%
0.0298 1
 
< 0.1%
0.0299 1
 
< 0.1%
0.03 6
< 0.1%
0.0301 6
< 0.1%
0.0302 1
 
< 0.1%
0.0303 4
< 0.1%
ValueCountFrequency (%)
102.6635 1
 
< 0.1%
101.3208 1
 
< 0.1%
96.6819 1
 
< 0.1%
89.5884 1
 
< 0.1%
86.7382 2
< 0.1%
84.2149 1
 
< 0.1%
83.3057 2
< 0.1%
80.9734 1
 
< 0.1%
80.1318 3
< 0.1%
78.6328 4
< 0.1%

R10 (MOhm)
Real number (ℝ)

Distinct6409
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.548408
Minimum0.0369
Maximum107.3251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:42.176342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0369
5-th percentile0.1184
Q17.8022
median24.2008
Q341.609
95-th percentile65.0981
Maximum107.3251
Range107.2882
Interquartile range (IQR)33.8068

Descriptive statistics

Standard deviation21.300897
Coefficient of variation (CV)0.80234178
Kurtosis-0.75745362
Mean26.548408
Median Absolute Deviation (MAD)17.2257
Skewness0.41946726
Sum7851612.1
Variance453.72822
MonotonicityNot monotonic
2022-12-20T14:11:42.336693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1204 1845
 
0.6%
0.1201 1814
 
0.6%
0.1202 1806
 
0.6%
0.1198 1749
 
0.6%
0.1199 1719
 
0.6%
0.1197 1715
 
0.6%
0.1205 1693
 
0.6%
0.1195 1575
 
0.5%
0.1206 1542
 
0.5%
0.1209 1482
 
0.5%
Other values (6399) 278807
94.3%
ValueCountFrequency (%)
0.0369 1
< 0.1%
0.0378 2
< 0.1%
0.0379 1
< 0.1%
0.038 2
< 0.1%
0.0381 1
< 0.1%
0.0384 1
< 0.1%
0.0385 2
< 0.1%
0.0386 1
< 0.1%
0.0387 2
< 0.1%
0.039 2
< 0.1%
ValueCountFrequency (%)
107.3251 2
 
< 0.1%
100.0304 1
 
< 0.1%
96.6431 1
 
< 0.1%
94.5698 2
 
< 0.1%
93.6563 1
 
< 0.1%
92.5828 2
 
< 0.1%
91.7066 3
 
< 0.1%
90.6767 5
 
< 0.1%
89.8357 10
< 0.1%
88.8467 20
< 0.1%

R11 (MOhm)
Real number (ℝ)

Distinct6229
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.611492
Minimum0.031
Maximum93.9401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:42.605540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.031
5-th percentile0.1081
Q110.76265
median28.1102
Q344.1024
95-th percentile65.8307
Maximum93.9401
Range93.9091
Interquartile range (IQR)33.33975

Descriptive statistics

Standard deviation21.400263
Coefficient of variation (CV)0.74796039
Kurtosis-0.83519291
Mean28.611492
Median Absolute Deviation (MAD)16.16
Skewness0.22314558
Sum8461763
Variance457.97126
MonotonicityNot monotonic
2022-12-20T14:11:42.754522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.109 2640
 
0.9%
0.1088 2593
 
0.9%
0.1085 2567
 
0.9%
0.1086 2563
 
0.9%
0.1089 2533
 
0.9%
0.1084 2407
 
0.8%
0.1092 2255
 
0.8%
0.1082 2133
 
0.7%
0.1093 1958
 
0.7%
0.1081 1774
 
0.6%
Other values (6219) 272324
92.1%
ValueCountFrequency (%)
0.031 1
 
< 0.1%
0.0313 1
 
< 0.1%
0.0316 1
 
< 0.1%
0.0317 1
 
< 0.1%
0.0319 1
 
< 0.1%
0.032 2
 
< 0.1%
0.0321 1
 
< 0.1%
0.0323 3
< 0.1%
0.0324 1
 
< 0.1%
0.0325 6
< 0.1%
ValueCountFrequency (%)
93.9401 1
 
< 0.1%
92.8633 1
 
< 0.1%
91.9845 6
 
< 0.1%
90.9514 10
 
< 0.1%
90.1079 12
 
< 0.1%
89.1159 26
< 0.1%
88.3056 20
 
< 0.1%
87.3522 35
< 0.1%
86.5731 42
< 0.1%
85.6562 58
< 0.1%

R12 (MOhm)
Real number (ℝ)

Distinct6251
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.815306
Minimum0.0336
Maximum116.3399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:42.908312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0336
5-th percentile0.1076
Q19.9752
median27.0806
Q341.4176
95-th percentile58.4435
Maximum116.3399
Range116.3063
Interquartile range (IQR)31.4424

Descriptive statistics

Standard deviation19.640626
Coefficient of variation (CV)0.73244086
Kurtosis-0.84129124
Mean26.815306
Median Absolute Deviation (MAD)14.9607
Skewness0.14114858
Sum7930546.2
Variance385.75417
MonotonicityNot monotonic
2022-12-20T14:11:43.066397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.109 1272
 
0.4%
0.1088 1256
 
0.4%
0.1091 1244
 
0.4%
0.1087 1225
 
0.4%
0.1093 1219
 
0.4%
0.1094 1139
 
0.4%
0.1086 1113
 
0.4%
0.1095 1106
 
0.4%
0.1084 1097
 
0.4%
0.1101 1077
 
0.4%
Other values (6241) 283999
96.0%
ValueCountFrequency (%)
0.0336 2
 
< 0.1%
0.0337 1
 
< 0.1%
0.034 3
< 0.1%
0.0341 1
 
< 0.1%
0.0342 5
< 0.1%
0.0343 1
 
< 0.1%
0.0344 1
 
< 0.1%
0.0346 5
< 0.1%
0.0347 3
< 0.1%
0.0348 1
 
< 0.1%
ValueCountFrequency (%)
116.3399 1
 
< 0.1%
90.2894 4
 
< 0.1%
89.2954 4
 
< 0.1%
88.4834 9
 
< 0.1%
87.5281 13
 
< 0.1%
86.7475 16
 
< 0.1%
85.8287 27
< 0.1%
85.0777 21
< 0.1%
84.1934 30
< 0.1%
83.4702 43
< 0.1%

R13 (MOhm)
Real number (ℝ)

Distinct6376
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.71304
Minimum0.0337
Maximum80.2041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:43.234900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0337
5-th percentile0.1014
Q18.0386
median22.5342
Q336.8913
95-th percentile55.8946
Maximum80.2041
Range80.1704
Interquartile range (IQR)28.8527

Descriptive statistics

Standard deviation18.320066
Coefficient of variation (CV)0.7725735
Kurtosis-0.73019722
Mean23.71304
Median Absolute Deviation (MAD)14.3571
Skewness0.33667113
Sum7013060.3
Variance335.62481
MonotonicityNot monotonic
2022-12-20T14:11:43.395445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1031 1208
 
0.4%
0.1034 1169
 
0.4%
0.1032 1158
 
0.4%
0.103 1156
 
0.4%
0.1029 1138
 
0.4%
0.1027 1106
 
0.4%
0.1035 1105
 
0.4%
0.1026 1071
 
0.4%
0.1039 1065
 
0.4%
0.1037 1064
 
0.4%
Other values (6366) 284507
96.2%
ValueCountFrequency (%)
0.0337 1
 
< 0.1%
0.0338 1
 
< 0.1%
0.0339 1
 
< 0.1%
0.0342 1
 
< 0.1%
0.0343 1
 
< 0.1%
0.0346 2
< 0.1%
0.0348 2
< 0.1%
0.0349 3
< 0.1%
0.035 2
< 0.1%
0.0351 1
 
< 0.1%
ValueCountFrequency (%)
80.2041 1
 
< 0.1%
79.5397 2
 
< 0.1%
78.7567 4
 
< 0.1%
78.1157 4
 
< 0.1%
77.3599 9
 
< 0.1%
76.7411 9
 
< 0.1%
76.0113 16
 
< 0.1%
75.4135 11
 
< 0.1%
74.7083 29
< 0.1%
74.1305 40
< 0.1%

R14 (MOhm)
Real number (ℝ)

Distinct6229
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.482101
Minimum0.0318
Maximum90.595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:11:43.569148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0318
5-th percentile0.1071
Q19.8574
median27.5851
Q346.2684
95-th percentile70.0976
Maximum90.595
Range90.5632
Interquartile range (IQR)36.411

Descriptive statistics

Standard deviation22.839372
Coefficient of variation (CV)0.77468603
Kurtosis-0.91852466
Mean29.482101
Median Absolute Deviation (MAD)18.411
Skewness0.29762062
Sum8719242.9
Variance521.63691
MonotonicityNot monotonic
2022-12-20T14:11:43.719209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1078 2449
 
0.8%
0.1079 2391
 
0.8%
0.1077 2365
 
0.8%
0.1075 2359
 
0.8%
0.1083 2359
 
0.8%
0.1082 2352
 
0.8%
0.108 2301
 
0.8%
0.1084 2192
 
0.7%
0.1074 2115
 
0.7%
0.1072 1966
 
0.7%
Other values (6219) 272898
92.3%
ValueCountFrequency (%)
0.0318 2
 
< 0.1%
0.0322 3
< 0.1%
0.0324 1
 
< 0.1%
0.0325 1
 
< 0.1%
0.0326 1
 
< 0.1%
0.0327 2
 
< 0.1%
0.0328 5
< 0.1%
0.0329 4
< 0.1%
0.033 6
< 0.1%
0.0333 2
 
< 0.1%
ValueCountFrequency (%)
90.595 1
 
< 0.1%
88.7468 1
 
< 0.1%
87.7697 4
 
< 0.1%
86.9716 5
 
< 0.1%
86.0327 14
 
< 0.1%
85.2654 29
 
< 0.1%
84.3623 45
 
< 0.1%
83.6241 51
 
< 0.1%
82.7549 100
< 0.1%
82.0441 148
0.1%

Interactions

2022-12-20T14:11:30.714223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:22.041743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:25.530473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:30.611848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:33.901435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:37.355933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:40.820756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:44.336186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:47.886532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:51.526503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:54.938113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:58.466838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:02.078379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:05.749711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:09.549204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:13.000843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:16.531127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:19.975672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:23.468554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:26.984280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:30.880940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:22.244278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:25.696517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:30.767903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:34.161444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:37.510799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:40.985819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:44.502888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:48.056949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:51.682465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:55.093951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:58.635229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:02.250732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:05.928919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:09.719403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:13.167655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:16.695515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:20.134693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:23.633544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:27.160351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:31.065127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:22.409502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:25.882719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:30.943458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:34.348427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:37.688633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:41.171838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:44.686120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:48.252179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:51.863938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:55.280069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:58.831529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:02.442071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:06.115207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:09.902913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:13.348856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:16.882630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:20.318903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:23.832475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:27.436028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:31.236057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:22.572381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:26.061646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:31.099587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:34.516376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:37.854762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:41.344250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:44.857363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:48.421060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:52.032361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:55.449258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:59.006829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:02.619145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:06.302016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:10.076510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:13.522453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:17.055902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:20.481118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:24.007570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:27.613894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:31.406796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:22.735752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:26.240261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:31.260914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:34.679529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:38.019346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:41.523488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:45.027887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:48.599385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:52.202057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:55.618892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:59.189524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:02.796330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:06.480204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:10.246066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:13.690344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:17.228257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:20.658592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:24.187474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:27.794718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:31.570637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:22.939552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:26.414350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:31.421220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:34.844815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:38.183182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:41.688384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:45.201796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:48.775823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:52.372577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:55.780106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:59.361523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:02.973672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:06.658507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:10.417223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:13.865041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:17.396563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:20.827575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:24.358146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:27.976162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:31.753460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:23.177583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:26.599838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:31.588300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:35.020895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:38.357209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:41.870502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:45.380233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:48.962923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:52.544626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:55.961107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:59.554459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:03.158793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:06.846287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:10.594724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:14.043920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:17.573465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:21.015678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:24.537980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:28.167671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:31.927039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:23.384840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:26.779851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:31.753419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:35.189655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:38.522386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:42.046021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:45.555274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:49.136193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:52.714121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:56.131235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:59.733421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:03.436056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:07.029005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:10.774958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:14.216227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:17.742033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:21.181517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:24.704732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:28.350743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:32.111667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:23.554476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:26.968586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:31.917828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-20T14:10:33.562935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:37.017162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:40.477958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:43.976381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:47.536804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:51.071653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:54.597154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:58.117942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:01.714293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:05.393746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:09.197391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:12.650195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:16.194809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:19.625077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:23.126297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:26.631398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:30.346649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:34.133554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:25.373538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:30.448333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:33.738393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:37.195106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:40.660645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:44.165611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:47.718503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:51.258189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:54.776851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:10:58.305101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:01.905343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:05.586122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:09.374072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:12.837248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:16.372902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:19.806160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:23.310494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:26.819832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:11:30.540563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-20T14:11:43.873741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-20T14:11:44.142594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-20T14:11:44.408998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-20T14:11:44.780087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-20T14:11:45.044815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-20T14:11:34.472244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-20T14:11:35.446229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
00.0000.051.081126.1138244.99630.89850.09640.15450.13380.11330.06070.07770.12120.11140.09960.12240.11150.06110.10860.1163
10.3080.048.140026.0600242.77540.89900.20020.57360.94090.73801.13381.31511.85481.00211.04121.45592.67504.39625.98987.5249
20.6170.048.140026.0600242.23250.21122.12705.35048.77726.51699.097410.169513.443823.602719.150624.200831.800937.127637.695246.7759
30.9250.048.140026.0600241.69130.207011.435424.283237.116224.212237.414034.005243.436558.423549.053067.098063.909064.986456.291966.8445
41.2350.048.140026.0600241.16190.204733.659554.460667.098041.418563.052465.946375.720181.369256.377570.335667.869776.861065.234075.5478
51.5420.048.140026.0600241.04830.202658.078778.303483.770348.743286.660384.844881.345372.638162.781180.693276.103373.704762.829273.0008
61.8520.048.140026.0600240.93360.201471.917682.004088.038850.736484.992286.611887.860983.030060.466772.670770.037074.959264.703673.6785
72.1610.048.140026.0600240.81930.200272.563882.004089.835751.827191.043083.910782.883068.298362.781172.126776.103368.006461.531971.7899
82.4710.048.140026.0600240.85130.200077.677080.509780.693249.094079.557176.210282.037273.234162.270670.335667.301468.488262.418173.6785
92.7810.048.140026.0600240.92080.200867.036875.619479.236937.237571.244472.226271.981066.068963.738869.222881.757064.037760.670171.7899
Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
29573790906.8050.061.6725.740.40310.200025.535719.523235.115025.230642.412141.526443.880365.582162.270664.659962.498055.092948.914672.4454
29573890907.1120.061.6725.740.10770.199222.684515.136232.569325.872243.700241.954943.637163.521660.071064.141666.834955.797248.117868.3837
29573990907.4220.061.6725.740.00000.200018.979911.401327.556724.882041.376040.073843.198068.298361.851461.907161.617354.099149.790070.0976
29574090907.7300.061.6725.740.00000.200616.24188.580924.060625.055141.999142.152642.767561.585060.071064.141665.295456.519547.584666.8445
29574190908.0390.061.6725.740.00000.199813.23266.477020.420024.711240.422739.125043.436568.932360.071059.746260.379156.921248.662868.9791
29574290908.3460.061.6725.740.00000.200010.94364.953817.255523.977241.376039.125041.931259.761458.763062.309260.379154.778545.784167.8952
29574390908.6550.061.6725.740.00000.20008.87323.883014.571124.466841.376037.829540.947563.978656.377563.212365.830754.099146.552265.2822
29574490908.9650.061.6725.740.00000.19977.23813.123411.897323.506139.478335.992332.367860.166457.582061.040159.116053.436146.783068.3837
29574590909.2730.061.6725.740.00000.19955.92842.57139.966223.818141.593834.888839.603660.659757.151263.212362.094753.735545.784164.8363
29574690909.5820.061.6725.740.00000.20004.88082.17078.188622.905138.607633.164239.973961.076955.624159.746258.326044.397345.562865.2822